1. Field of the Invention
The present invention relates to an apparatus, which can be utilized in preprocessing of signals in, e.g., plant control, and performs signal processing such as clustering or identification of signals, conversion from an analog signal to a digital signal, and the like, using a neural network.
2. Description of the Related Art
When various plants are to be managed and controlled, the temperatures of respective units of a plant are measured by thermocouples or temperature-measuring resistors. However, the thermal electromotive force values or resistance values, and the temperature do not have a fine linear relationship therebetween. When the thermal electromotive force values or resistance values are linearly temperature-converted, accurate values cannot be obtained.
Signals detected by a sensor are clustered according to their magnitudes, and the clustered signals are temperature-converted according to correction formulas determined in correspondence with classes to which the signals belong.
In the clustering, comparison between detection signals and boundary values of each class is repeated for all the classes.
In management and control of steam engines, an enthalpy value must be known from, e.g., the steam pressure on the basis of a steam table. In this case, functions are provided in units of regions of input variables, and comparison between an input variable and a reference value is repeated to determine a region where the input variable is present. Thereafter, the enthalpy value is calculated.
In general, plant management and control are performed by digital computers. Various state amounts of a plant are mainly output as analog signals from sensors. For this reason, A/D converters for converting analog signals into digital signals are used.
More specifically, an analog signal output from a sensor is compared with an analog signal corresponding to the most significant bit of a binary number to determine the relationship therebetween. Then, the analog signal is compared with an analog signal corresponding to the next bit of the binary number to determine the relationship therebetween. Such processing is repeated up to the least significant bit of the binary number, thereby converting the analog signal into a digital signal of a predetermined number of bits.
In inference processing in, e.g., an expert system, collation of bit patterns is repeated to find out a production rule to be applied to that case.
A system for determining a synapse coupling coefficient and a bias input value of a mutual-coupled type neural network on the basis of an energy function so as to convert an analog signal into a binary digital signal of a plurality of bits is also known.
In the above-mentioned various signal processing operations, in order to linearize an output signal from a temperature sensor to obtain a correct temperature signal, to obtain a digital signal by A/D conversion, to calculate an enthalpy value, or to find out a production rule matching with a condition, repetitive processing is required. The repetitive processing requires a long period of time until it is completed.
For this reason, such a signal processing method cannot be applied to fields requiring quick processing. Because, when an alarm is delayed, plant operations may be considerably disturbed, or when a trip is delayed, a plant may be damaged. When the signal processing method is used in a feedback control system, since a dead time undesirably exists in a loop, improvement of controllability is limited. In particular, when high-speed control is necessary, a delay time caused by signal processing is not allowable.
In an A/D converter using a conventional neural network, in order to realize synapse coupling a large number of high-precision resistors having different resistances are required. This fact reveals that such an A/D converter is difficult to realize in fields requiring an A/D converter of a large (12-16 bit) number of digits like in a plant instrumentation control field. In particular, this point becomes a major obstacle when a neural network is realized by the LSI technique.